Search and retrieval of objects in a social networking system

ABSTRACT

A social networking system receives a query associated with a user and, in response, provides a combined result set comprising objects stored by a social networking system that match the query. The combined result set comprises multiple result sets obtained from different search algorithms. The various objects stored by the social networking system may be of different types representing different concepts, such as user objects, application objects, event objects, location objects, group objects, and hub/page objects, any of which may be included in the result set. The objects of the result set may be further filtered, ordered, and/or grouped based at least in part on known relationships of the user with the objects, such as geographic distances between locations associated with the user and the objects. 
     In one embodiment, one of the search algorithms identifies second-order connections of the user by referring to a connection index that stores a list of the connections of the users. The search algorithm may also identify a number of mutual connections that the user shares with the second-order connections.

CROSS REFERENCE TO RELATED APPLICATIONS

The application claims the benefit of Provisional Application No.61/267,777, filed on Dec. 8, 2009, which is incorporated herein byreference.

BACKGROUND

The present invention generally relates to the field of computer datastorage and retrieval, and more specifically, to performing searches forobjects within a social networking system.

Modern computing systems store vast amounts of data, and as aconsequence it has become increasingly important to provide users witheffective ways to locate information that is relevant to theirinterests. One area in which large amounts of information are involvedis social networking. Social networking systems allow users to designateother users as friends (or otherwise connect to or form relationshipswith other users), contribute and interact with media items, useapplications, join groups, list and confirm attendance at events, createpages, and perform other tasks that facilitate social interaction. Sinceeach of these tasks may involve various data objects, social networkingsystems are a good example of the demand for systems that help userslocate relevant information from within a large set of informationtracked or otherwise used by the system.

Although it might be helpful to customize a search for a user'sparticular needs, many systems have little or no relevant informationabout the user who is searching for information, which makes locatinginformation of particular relevance to a given user more challenging.But a social networking system typically tracks a considerable amount ofinformation about each user, which can be leveraged to identify items ofrelevance to a given user. However, the query functionalities currentlyavailable on social networking systems do not fully leverage theinformation that the systems store. Nor do other systems use theinformation stored by social networking systems to improve the relevanceof the data provided to their own users.

SUMMARY

Embodiments of the invention improve the ability of users of a socialnetworking system to search for information that is likely to berelevant to them by leveraging the social information about those users.In one embodiment, a social networking system receives a query, such asa textual query, from a client via a client device. In response to thequery, the social networking system produces a result set of objectsstored by the social networking system and that match the query.

The various objects stored by the social networking system may includeuser objects, application objects, event objects, group objects, webpage objects, location objects, hub/page objects, and/or any of avariety of types of objects that may be tracked or maintained by thesocial networking system. The combined result set may combine objects ofone or more of the different types stored by the system that match theuser's query. The entry of a query thus provides the user with a singlesearch interface for obtaining any objects in the social networkingsystem. The user can then interact with these objects according to thefunctionality provided by the social networking system for their variousobject types. For example, a message can be sent to the usercorresponding to a user object of the result set, a group correspondingto a group object can be joined, and the like.

In one embodiment, the social networking system represents the objectsin a social graph—that is, as a set of nodes connected by edges, eachnode corresponding to an object. The edges between the nodes representrelationships between the objects, such as actions taken by one objecton another object.

In one embodiment, the social networking system performs a plurality ofsearch algorithms using the query. The search algorithms may include,for example, a search of objects that have a certain degree ofrelationship with the user providing the query, such as beingfriends-of-friends. The search algorithms may also include a search ofobjects in a history of the user providing the query, such as recentlyviewed objects. The search algorithms may further include a search ofobjects determined to be globally important across the social networkingsystem as a whole. The results for each of the search algorithms areaggregated into a combined result set and then presented to the user. Inone embodiment, the objects of the combined result set are filtered,ordered, and/or grouped based at least in part on known relationships ofthe user providing the query with the objects—such as a friendshiprelationship with a user object, a number of friends in common with auser object, prior uses of an application object, membership of an eventobject, and the like—or a measure of an affinity of the user for theobject, such as a geographic distance between locations associated withthe user and the object, a graph distance in the social graph betweenthe user and the object, and a similarity measure between the user andthe object.

In one embodiment, one or more of the search algorithms perform a prefixsearch in which the search matches objects with name tokens, such as afirst or last name associated with a user object, for which the query isa prefix. The prefix search may be performed again for each newcharacter that the user enters into a search interface, such that thecombined result set may contain fewer matching objects. For example,when the user types additional characters into the search interface,fewer objects may match the (now longer) typed query, and hence thecombined result set would include fewer objects.

In one embodiment, the search algorithms include a search ofsecond-order connections of the searching user who provided the query,such that the second-order connections match the query. The second-orderconnections of the searching user are those objects in the social graphwith two edges separating them from a user object corresponding to thesearching user—i.e., the first-order connections of the first-orderconnections of the searching user. In one embodiment, the socialnetworking system stores a connection index comprising a set ofconnection tables, each connection table corresponding to one of theusers of the social networking system and indicating the connections ofthat user. In response to a query of the searching user, the first-orderconnections of the searching user—such as the searching user's directfriends—are determined. Then, based at least in part on the connectiontables corresponding to the first-order connections, second-orderconnections are identified and a number of connections in common withthe searching user are identified for each second-order connection. Thenumber of connections in common may be used to order the second-orderconnections in a search result. In one embodiment, each connection tablestores a set of name tokens, such as first and/or last names of userobjects, sorted to allow rapid prefix searching of the name tokens forthe query.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a high-level block diagram of a computing environment,according to one embodiment.

FIG. 2A illustrates a query entered by a user in a search interface of asocial networking system, according to one embodiment.

FIG. 2B illustrates a query entered by a user in a user interface of thesocial networking system, according to one embodiment.

FIG. 2C illustrates use of a query entered by a user in a user interfaceof a third party website separate from the social networking system,according to one embodiment.

FIG. 3 is a flowchart of a process for obtaining and displaying objectsin response to a search query, according to one embodiment.

FIG. 4 is a flowchart of a process for obtaining second-orderconnections in response to a search query, according to one embodiment.

FIG. 5 illustrates example tables of a connection index used to obtainsecond-order connections, according to one embodiment.

The figures depict embodiments of the present invention for purposes ofillustration only. One skilled in the art will readily recognize fromthe following description that alternative embodiments of the structuresand methods illustrated herein may be employed without departing fromthe principles of the invention described herein.

DETAILED DESCRIPTION

System Architecture

FIG. 1 is a high-level block diagram of a computing environmentaccording to one embodiment. FIG. 1 illustrates a social networkingsystem 100, a client device 180, and a third party website 190 connectedby a network 170. A user of the client device 180 interacts with thesocial networking system 100 via an application such as a web browser,performing operations such as browsing content, posting messages,performing queries for people or other content of interest, and thelike. Additionally, the third party website 190 can also interact withthe social networking system 100 via a system-provided applicationprogramming interface (API) 150. For example, the third party websitecan perform operations supported by the API, such as performing queriesto obtain information stored by the social networking system 100.

The social networking system 100 comprises a number of components usedto store information on objects represented in or by the socialnetworking environment, and on the relationships of the objects. Thesocial networking system 100 additionally comprises components to enableclients of the system—such as a human user of the client device 180interactively using the system, or a component of the third partywebsite 190 requesting information—to query the system for informationof interest.

More specifically, the social networking system 100 comprises an objectstore 110 that stores information on various objects tracked by thesocial networking system 100. These objects may represent a variety ofthings with which a user may interact in the social networking system100, including, without limitation, other users 111 of the socialnetworking system (represented, e.g., as a profile object for the user),applications 112 (e.g., a game playable within the social networkingsystem), events 113 (e.g., a concert that users may attend), groups 114to which users may belong, pages or hubs 115 (e.g., pages constituting aparticular organization's presence on the system), items of media 116(e.g., pictures, videos, audio, text, or any other type of mediacontent), locations 117 associated with a user (e.g., “Santa Clara,Calif., USA”), and even concepts 118 or other terms (e.g., “Victorianliterature”). The object store 110 may further store objectsrepresenting other data routinely produced by users of the socialnetworking system 100, such as inbox messages, status updates, photos,videos, comments, notes, and postings. An object in the object store 110may represent an entity existing within the social networking system(e.g., an application 112 available on the social networking system), avirtual entity that exists outside the domain of the social networkingsystem (e.g., a website), or a real-world entity (e.g., a sports team ora retail store).

The object store 110 may store all of the objects existing within thesocial networking system 100, such as the code of an application 112, orthe image data associated with an image media item 116. Alternatively,for virtual entities existing outside of the social networking system100, the object store 110 may contain some form of pointer or referenceto the entities, such as the uniform resource locator (URL) of anexternal media item 116. Additionally, the object store 110 may alsostore metadata associated with the objects, such as a name describingthe object (e.g. “Charles Williams” for a person, “Birthday Reminder”for an application, or “Penguin Fanciers” for a group), an imagerepresenting the object (e.g., a user profile picture), or one or moretags assigned to the object by users (e.g. the textual strings “game”,“crime”, and “strategy” for a strategy game application). Differenttypes of objects may have different types of metadata, such as a set ofassociated users 111 for a group 114, a media type (e.g., “video”) for amedia item object 116, and a unique user ID and name tokens (e.g.,separate first and last names “Charles” and “Williams”) for a userobject 111.

In one embodiment, the social networking system 100 further comprises agraph information store 120 that represents the objects of the objectstore 110 as nodes that are linked together in a “social graph.” Thegraph information store 120 thus comprises information about therelationships between or among the objects, represented as the edgesconnecting the various object nodes. Various examples of edges in thesocial graph include: an edge between two user objects representing thatthe users are have a relationship in the social networking system (e.g.,are friends, or have communicated, viewed the other's profile, orinteracted in some way), an edge between a user object and anapplication object representing that the user has used the application,an edge between a user object and a group object representing that theuser belongs to the group, and an edge between a user object and a pageobject representing that the user has viewed the page, to name just afew. For example, if one user establishes a relationship with anotheruser in the social networking system, the two users are each representedas a node, and the edge between them represents the establishedrelationship; the two users are then said to be connected in the socialnetwork system. Continuing this example, one of these users may send amessage to the other user within the social networking system. This actof sending the message is another edge between those two nodes, whichcan be stored and/or tracked by the social networking system. Themessage itself may be treated as a node. In another example, one usermay tag another user in an image that is maintained by the socialnetworking system. This tagging action may create edges between theusers as well as an edge between each of the users and the image, whichis also a node. In yet another example, if a user confirms attending anevent, the user and the event are nodes, where the indication of whetheror not the user will attend the event is the edge. Using a social graph,therefore, a social networking system may keep track of many differenttypes of objects and edges (the interactions and connections among thoseobjects), thereby maintaining an extremely rich store of sociallyrelevant information.

In one embodiment, edges in the graph information store 120 haveassociated metadata, such as a label describing the type of relationship(e.g., “friend” as the label between two user objects), or a valuequantifying the strength of the relationship. Further, a relationshipdegree, or “distance,” between any two objects can be ascertained bydetermining the number of edges on the shortest path between theobjects. For example, two user objects that have an edge between them(e.g., denoting a friendship relationship) have a relationship degree(or “distance”) of one and are considered first-order connections.Similarly, if a user object A is a first-order connection of user objectB but not of user object C, and B is a first-order connection of C, thenobjects A and C have a relationship degree of two, indicating that C isa second-order connection of A (and vice-versa).

In one embodiment, the social networking system 100 adds information tothe graph information store 120 in real time as it observes eventstaking place indicating relationships between the various objects, suchas a user 111 interacting with an application 112. Alternatively and/oradditionally, the graph information store 120 may be created based onexisting stored information from which relationships can be inferred.For example, the friend list of a user 111 might indicate that the userhas a friend relationship with another user, or the data for a group 114might indicate that some set of users has a membership relationship withthat group, and this information could be reflected in the graphinformation store 120.

U.S. application Ser. No. 12/193,702, filed Aug. 18, 2008, which isincorporated by reference in its entirety, describes an example of asocial networking system that maintains data for a number of differenttypes of objects used by the social networking system. U.S. applicationSer. No. 12/485,856, filed Jun. 16, 2009, which is incorporated byreference in its entirety, describes embodiments of a social networkingsystem that maintains objects and connections among the objects in asocial graph.

In one embodiment, the social networking system comprises a connectionindex 121 that stores a subset of the information in the graphinformation store 120 in a manner that allows rapid queries of thatinformation. For example, in one embodiment the connection index 121stores, for each user, a list of all of the connections between thatuser and other users. This allows, for example, a rapid determination ofother users to whom the user has a direct or indirect connection (e.g.,friends-of-friends) and the order of their connection (e.g., 2, forfriends-of-friends). The use of the connection index 121 is discussedfurther below with respect to FIGS. 4-5.

The social networking system 100 further comprises a query processingmodule 140 that identifies objects from the object store 110 that matcha particular search query. In one embodiment, the searching userdirectly provides a textual query by typing text into a text areaassociated with query functionality of a user interface. In otherembodiments the user provides a non-textual query that is translated toa textual query, such as selecting a list item with associated text thatserves as the search query text. The search query can be issued in auser-specific query context, such as when a searching user has logged into the social networking system 100 either from a web site of the socialnetworking system or from the third party website 190 that accesses dataof the social networking system. In a user-specific context, the socialnetworking system 100 can leverage the information that it stores aboutthe searching user and about the various objects in the object store 110to predict one or more objects that are likely to be of interest to thequery user, given the search query or a portion thereof.

The query processing module 140 contains a number of sub-modules 141-143that identify objects according to different search algorithms, anaggregation sub-module 145 that combines the results from the varioussub-modules, and filtering and ordering sub-modules 146 and 147 thatmodify the contents and/or order of the combined results. These varioussub-components of the query processing module 140 are now described inmore detail.

The second-order connections search sub-module 141 identifiesfirst-order and second-order user connections of the searching user thatmatch the query. Specifically, the second-order connections searchsub-module 141 identifies a set comprising the first-order connectionsof the searching user having object types of interest, such as userobjects 111 and page objects 115. The second-order connections searchsub-module 141 then further identifies a set of all the first-orderconnections of the objects within the identified set (other than thesearching user itself), this latter set of connections constituting thesecond-order connections of the searching user. Finally, thesecond-order connections search sub-module 141 produces, as its resultset, all of the identified first-order and second-order connections thatmatch the query according to some query matching algorithm, such as aprefix substring match. In one embodiment, the second-order connectionssearch sub-module 141 performs a separate search, and produces aseparate result set, for each of the types of interest, such as onesearch and result for user objects 111, one for page objects 115, andthe like. Alternatively, the second-order connections search sub-module141 may return a single result set containing both the first-order andsecond-order connections, or it may return a separate result set foreach.

It is appreciated that although the second-order connections searchsub-module 141 has been described as returning first- and second-orderconnections, any number of orders of connection could be returned. Forexample, the sub-module could also return connections of third order, inaddition to those of first and second order.

In one embodiment, in order to decrease the time needed for the search,the second-order connections search sub-module 141 performs the searchwith reference to the connection index 121. This use of the connectionindex 121 is described in more detail with respect to FIGS. 4-5, below.

The history search sub-module 142 identifies, among objects stored inthe history of the searching user, objects that match the query. In oneembodiment the history for the search user is not stored within thesocial networking system 100 itself, but rather is cached on the clientdevice 180 of the particular searching user 111 to whom it pertains.That is, as a particular searching user represented by a user 111 in theobject store uses his or her client device 180 to communicate with otherusers 111, to use applications 112, and the like, the correspondingobjects are cached on the client device. Similarly, portions of theconnection index 121, such as first-order user connections of thesearching user, can likewise be cached on the client device 180.Regardless of the exact storage location of the history for thesearching user, the history search sub-module 142 examines each of theobjects within the history and identifies those matching the query,according to some match algorithm.

The global importance search sub-module 143 identifies, among objectsconsidered to be of global importance, objects that match the query. Theglobally important objects need not have any specific relationship withthe searching user, but rather are considered to be of general interestacross users of the social networking system 100 as a whole. The objectsto be placed within the globally important group 131C may be identifiedin different ways. For example, objects may be considered to be globallyimportant if they have been accessed (e.g., viewed), tagged, posted,marked as having one or more fans, or otherwise designated as being ofinterest, some pre-specified number of times. Alternatively, actionssuch as accessing/tagging/posting may be used to calculate a score, andthe objects with the top N scores may be selected as globally importantobjects, for some integer N. In one embodiment, the globally importantobjects, or references thereto, are stored in a distinct portion of theobject store 110 so that they are readily available for searches by theglobal importance search sub-module 143. The global importance searchsub-module 143 then selects as its result set, from among the objects ofglobal importance, those objects that match the query according to somematch algorithm.

In one embodiment, the match algorithm used by one or more of thesub-modules 141-143 is a case-insensitive prefix search that comparesthe query with prefixes of various portions of the metadata, such as thefirst name, last name, and/or nickname of a user object 111, a title ofan application object 112, and the like. The match algorithm, or anothercomponent of the social networking system 100, may additionally processthe query text and/or the metadata of the various objects prior toperforming the match in order to allow provide greater flexibility inthe matching. For example, the query processing module 140 could takeinto account that the first name “Mike” is a well-known alternative forthe first name “Michael,” such that typing “Michael” would match “Mike”,as well, even though “Mike” does not contain the prefix “Michael.” Insome embodiments, the match algorithm additionally compares the query todata other than object names, such as words in tags assigned to objects,or synonyms of such words.

The aggregation sub-module 145 receives the result sets produced by eachof the search sub-modules 141-143 and aggregates them into a singlecombined result set to be presented to the user. The aggregation module145 removes duplicates of objects that occur multiple times across thevarious result sets, such as a user object 111 that occurs in the resultsets for the second-order connections search sub-module 141 and for thehistory search 142.

In one embodiment, the filtering sub-module 146 filters the combinedresult set based on an affinity measure of the searching user for eachof the objects. In different embodiments, the affinity measure is afunction of one or more of a physical distance (e.g., a residencelocation of a user object in the matching set must be within N miles ofa residence location of the searching user), a graph distance betweenthe user object 111 of the searching user and objects of the matchingset on the social graph (e.g., the distance must be 2 or less), and asimilarity measure.

The similarity measure quantifies how likely the searching user would beto find a particular matching object to be of interest, and may becalculated in different ways in different embodiments. For example, thesimilarity measure may be calculated by comparing a user profile of thesearching user to the matching object, such as by noting that the userprofile states that the searching user enjoys golf and that the matchingobject is a group 114 devoted to golf. Alternately and/or additionally,the similarity measure may be calculated by determining interests of thesearching user based on past actions of the searching user within thesocial networking system, such as posting messages related to golf orusing golf game applications 112, and comparing the determined intereststo information about the matching object. The physical distance, graphdistance, and the similarity measure can be considered, individually orcollectively, to constitute an “affinity” of the searching user for theobject in question.

The filtering module 146 may further filter the matching objects basedon additional criteria. For example, when the query processing module140 is matching objects on behalf of a third party website 190 thatrents movies, it could filter the identified objects to include onlythose objects having the keyword “movie” (or synonyms such as “film”),within their metadata, for example. Embodiments may use any of a varietyof criteria for filtering the search results based on contextualinformation about the system in which the search interface is presentedto the user.

In one embodiment, the ordering module 147 further imposes an orderingon the initial and/or filtered matching objects of the combined resultset. The order may be based on the affinity measure discussed above,e.g., with geographically closer objects ordered higher thangeographically farther objects. Other factors may additionally and/oralternatively be taken into consideration when determining the order,such as the search sub-module 141-143 from which an object came, e.g.,user-specific objects returned by the history search sub-module 142tending to be ordered more highly than those in the non-user-specificglobally important group 131C.

Additionally, the query ordering module 147 can divide the matchingobjects into groups and order each group separately, each group havingits own ordering criteria. The groups may correspond to the variousresult sets provided by the search sub-modules 141-143, or the groupsmay be defined in other ways. For example, user objects 111 from theresult set provided by the second-order connections search module 141may be displayed in a “friends” group separate from objects of othergroups and ordered according to the graph distance of the variousfriends. Further, first-degree friends (friends with graph distance 1)may be placed into a separate sub-group ordered according to geographicdistance of the friends, and second-degree friends may be placed into asub-group ordered according to a number of mutual friends.

In one embodiment, the filtering and/or ordering operations may beperformed by the search sub-modules 141-143, rather than by (or inaddition to) the filtering and ordering modules 146-147. For example,the second-order connections search sub-module 141 can group thefirst-order and second-order connections and order the second-orderconnections according to numbers of mutual connections.

The query processing module 140 may select the information about theobjects that are returned in the combined result set, such as selectingassociated metadata such as object names or profile pictures, the actualcontent data associated with the object (e.g., the video data of a videomedia item), or some combination of the two.

The operations of the various query processing sub-modules 141-147 neednot be static, but may be determined in a dynamic manner based onoptions provided along with the query. That is, the objects that arereturned in the various result sets, their various groupings and order,the filtering criteria that are application to them, the associatedinformation that is provided on them, and the like, may all be specifieddynamically based on options provided along with the query. For example,the query might include not only the query text itself (e.g., the string“Eli” in the example above), but also an option specifying that onlyobjects from the result sets provided by the second order connectionssearch sub-module 141 and the global importance search sub-module 143should be included, and an option specifying that the users from thesecond order connections search sub-module should be grouped withfirst-order connections preceding second-order connections, where theformer is ordered according to geographic proximity and the latter isordered according to number of mutual connections, for example. In oneembodiment, the options may be specified by the user, either directly,or indirectly based on pre-specified user preference settings.

As described above, the social networking system 100 provides the searchfunctionality to its users within the system 100. In other embodiments,however, the social networking system 100 may export this searchcapability to third-party systems, thereby expanding aspects of thesocial networking environment outside of the actual social networkingsystem 100. For example, the social networking system 100 may comprisean application programming interface (API) 150 used to accessinformation stored by the social networking system, such as the variousobjects of the object store 110. For example, the functionality of thequery processing module 140 can be exposed to other applications via theAPI 150. In one embodiment, the API can be accessed both locally andremotely. For example, the API 150 may be accessed via a locally-loadedmodule, such as a DLL, or it may be may be accessed remotely as a webservice by a third party website 190 or other remote system. Embodimentsdescribing use of a social networking system's functionalities bysystems outside of the social networking system are described in U.S.application Ser. No. 12/324,761, filed Nov. 26, 2008, and U.S.application Ser. No. 12/508,521, filed Jul. 23, 2009, both of which areincorporated by reference in their entireties.

Use with Example User Interfaces

FIGS. 2A-2C are screenshots illustrating different search contexts anddisplays of search results using the query functionality of the queryprocessing module 140, according to one embodiment.

FIG. 2A illustrates a query entered by a user in a search interface ofthe social networking system 100. Specifically, FIG. 2A depicts a searchbar 205 comprising a search query area 206 into which a user can enter atextual query string and receive a set of matching objects 210. In theexample of FIG. 2A, the searching user has entered the query “michael”,presumably searching for users 111 of that name. In response, the set ofmatching objects 210 is displayed, the matching objects all having theprefix “michael,” (or “mike,” a known equivalent thereof).

In the example of FIG. 2A, the matching objects 210 are displayed ingroups, according to their type and/or relevance to the user whospecified the query. For example, the first nine objects are users 111that are first-order or second-order connections of the searching userand are found by performing a prefix search of the connection index 121.Of these nine users, four are in a sub-group of first-order connections210A, and the remaining five are in a sub-group of second-orderconnections 210B. Each user 111 is displayed based on its degree ofrelationship with the searching user, with first-order connectionsdisplaying their addresses, and second-order connections displaying howmany mutual friends they have in common with the searching user. Thelast of the matching objects 210, an object 210C for a page 115dedicated to Michael Jackson, is not of specific relevance to thesearching user—e.g., is not a first-order or second-order connection ofthe user—but is considered of global importance given its sheeraggregate popularity.

Within each group, the order of the various matching objects 210 can bedetermined based on the relevance of the object to the searching user,as calculated from the information on the searching user tracked by thesocial networking system 100. For example, as previously noted, thematching first- or second-order connections of FIG. 2A are ordered basedon their degree of relationship, with the first-order connectionsdisplayed before the second-order connections. Further, FIG. 2A depictsanother potential ordering based at least in part on a geographicproximity to the searching user. For example, for a searching user knownto be from the state of Washington, the first-degree friends 210A areordered such that friends also located in Washington precede those fromother states, such as Nevada or California. Additionally, thesecond-order connections 210B are ordered based on their number ofmutual connections in common with the searching user, with second-orderconnections with more mutual connections located higher in the resultset.

FIG. 2B illustrates a query entered by a user in a user interface of thesocial networking system 100. More specifically, FIG. 2B depicts aportion 215 of a user profile listing the user's personal information,including a text field 216 for entering text specifying the user'sfavorite music. In a conventional user interface, the user would beobliged to enter the entire text string corresponding to the user'sfavorite music, but in the interface of FIG. 2B the social networkingsystem 100 performs an implicit query as each character of the string istyped. In the depicted example, the user has typed the string “green d”,and in response the page objects 220A with names “Green Day” and “GreenDharma” have been displayed, along with second-order connections 220B.If the user typed an additional character, such as ‘h’ (thereby formingthe query string “green dh”), the query processing module 140 mightnarrow the search results 220 to contain only the page object for “GreenDharma.”

Note that the page objects 220A have been ordered above the second-orderconnections 220B, with the page objects further ordered based on numberof fans (popularity), as is appropriate for a profile field in which theuser is more likely to specify a concept or a group (e.g., a band or amusical genre) rather than an individual person. This ordering of groups114 before users 111—or the ordering of the users before groups in FIG.2A—could be specified, along with other rules, as part of the call tothe API 150 through which the query processing module 140 is accessed.For example, scripting code within the web pages embodying the userinterfaces of FIGS. 2A and 2B could call the API 150, specifying therules by which the query results are to be ordered as parameters of thecall. This permits different user interfaces to order the results indifferent ways appropriate to the context in which they are used.Similarly, API-specified rules could permit the exclusion of certaintypes or classes of objects, as appropriate for the context in which theobjects are being matched. For example, when suggesting profile valuesfor favorite music, as in the example of FIG. 2B, the social networkingsystem 100 could specify that objects for groups 114, hubs/pages 115,media items 116, and concepts 118 should be included as matchingobjects, but that objects for users 111, applications 112, events 113,and locations 117 should be excluded on the assumption that they areunlikely to represent a preferred type of music or a particularpreferred artist.

The query need not be specified within a user interface of the socialnetworking system 100 itself. Rather, a third party website or othersystem may use an application programming interface (API) of the socialnetworking system 100 to gain access to the information stored by thesocial networking system, thereby enhancing the information that thethird party website provides. FIG. 2C illustrates use of a query enteredby a user in a user interface of a third party website 190 separate fromthe social networking system 100, according to one embodiment. Morespecifically, FIG. 2C represents a portion 235 of a web-based userinterface of a movie rental website 190, the website leveraging theinformation provided by the social networking system 100 to provideuseful movie suggestions 240, such as suggestions tailored to aparticular user.

In FIG. 2C, a user has entered the string “cyr” into a textual searchfield 236. In response, the third party website 190 has called the API150 of the social networking system 100, e.g., using a web serviceinterface, has received a number of objects from the query processingmodule 140, and has displayed a number of suggestions 240 based on thereceived objects. As described above with respect to FIG. 2B, parameterscan be provided along with the query that specify what types of objectswill be matched, how many objects will be matched, how the objects willbe ordered in the matching set, and the like. The third party website190 can present this information directly—i.e., showing each matchingobject in the same order as it was provided—or indirectly—e.g., showinga set of items derived from the objects.

As an example of indirect use of the matching objects, the set ofobjects matching the query “cyr” might include a user object 111 for afirst-order user connection of the user named Slobodan Cyrcic andobtained from the result set of the second-order connections searchsub-module 141, a hub/page object 115 about the play/film “Cyrano deBergerac” obtained from the result set from the history searchsub-module 142, and a group object 114 about the actress Miley Cyrusobtained from the globally important group 131C in the query groupstore. The movie rental website 190 could then determine that it hadvarious titles related to the hub/page object “Cyrano de Bergerac,”including the film “Cyrano de Bergerac” and the compilation “Cyrano deBergerac/The Son of Monte Cristo,” and various titles and/or categoriesrelated to the group object 114 devoted to Miley Cyrus, including theDVD “The World According to Miley Cyrus” and a “Hannah Montana tour”DVD. Additionally, the movie rental website 190 could list other titlesor categories not derived from one of the matched objects, such as DVDsfor the films “Cyrus” and “Being Cyrus” and search results for theactress Catherine Cyran, as found in its own inventory databases.

The third party website 190 could also use additional information storedby the social networking system 100 to provide better suggestions 240.For example, the third party website 190 could additionally obtaininformation from the query user's profile via the API 150 and analyze itto determine that the searching user is (for example) a middle-aged fanof foreign films. The website 190 could accordingly order thesuggestions 240 so that those related to the foreign film “Cyrano deBergerac” are placed high in the list, and those related to theyouth-oriented Miley Cyrus items are placed low in the list.

Even if the searching user had not identified himself or herself bylogging in or otherwise authorizing the third party website 190 toaccess the user's information on the social networking service 100, thethird party website could still obtain non-user-specific informationuseful for formulating suggestions 240. For example, the third partywebsite 190 would still have access to objects such as those provided bythe global importance search sub-module 143, even if it lacked access tothe user-specific objects provided by the second-order connectionssearch 141 and the history search 142.

Process of Object Search and Retrieval

FIG. 3 is a high-level flowchart of a process 300 for obtaining anddisplaying objects in response to a search query, according to oneembodiment. Initially, a query is received 310 by the social networkingsystem 100. In one embodiment, the query is received from a userinterface of the social networking system 100 itself, such as within animplicit or explicit search interface illustrated in FIGS. 3A-3B. Inanother embodiment, the query is received from the third party website190.

The social networking system 100 identifies 320 objects from the objectstore 110 that match the received textual query. More specifically,various result sets are received from the various search algorithms ofthe search sub-modules 141-143, as described above with respect toFIG. 1. These result sets are then aggregated (e.g., by the aggregationmodule 145) into a single combined result set, with duplicate objectsbeing removed.

Each of the matching objects may further be filtered 330 based on anaffinity measure of the searching user for the objects. In differentembodiments, the affinity measure may comprise: a physical distancebetween a geographic location associated with the user with whom thequery is associated and a geographic location associated with theobject, a distance in a social graph between the object and a userobject of the searching user, and a general similarity measuredetermined based on, for example, a user profile of the searching user,or past actions of the searching user within the social networkingsystem 100. Other data may be used to measure affinity for matchingobjects.

The resulting objects are then ordered 340 and/or grouped. As oneexample, the objects can be grouped according to a source from which theobjects came, such as from the result sets provided by the second orderconnections, history, or global importance search sub-modules 141-143.The objects can further be ordered 340 based on the affinity measure,and/or upon other factors, such as the source from which they came.Alternatively and/or additionally, the various search algorithms bywhich the objects are identified 320 may themselves perform filtering,ordering, and grouping operations.

Finally, the objects are displayed 350. In one embodiment, the objectsare displayed in the user interface in which the query was entered, suchas the user interfaces of the social networking system 100 depicted inFIGS. 3A-3B, or the user interface of the third party website 190depicted in FIG. 2C.

FIG. 4 is a flowchart of a process 400 for obtaining second-orderconnections in response to a search query, according to one embodiment.Specifically, this process explains in more detail the steps performedby the second-order connections search sub-module 141 when identifyingmatching objects during step 320 of FIG. 3. By this point, a textualquery has already been received 310.

The process 400 identifies 410 the first-order connections of thesearching user by referring to the connection index 121 of FIG. 1. Thestructure of the connection index 121 is now explained with respect toseveral examples tables thereof.

FIG. 5 illustrates example tables of the connection index 121 used toobtain first-order and second-order connections, according to oneembodiment. Specifically, the example of FIG. 5 illustrates three tables501, 502, and 503 corresponding to some set of user objects 111 forfictitious users named “Christian Konig,” “David Eliot,” and “ThomasStearns Eliot,” respectively. Each table contains data identifying thefirst-order connections of the user to whom the table corresponds. Morespecifically, each table comprises a set of name tokens, one name tokenfor each distinct portion of the names of the users represented by thetable. In one embodiment, the name tokens are sorted alphabetically toallow quick name lookups using a binary search algorithm. In oneembodiment, a Bloom filter summarizing the name tokens is employed toprovide a rapid determination of whether a given name is containedwithin the name table. Each table further comprises a set of unique userIDs, each user ID representing a distinct user, and each name tokenbeing associated with one of the user IDs. FIG. 5 illustrates integeruser IDs, but any unique identifier may be used.

In the depicted example, the table 501—listing the users directlyrelated to a user named “Christian Konig” and having a user ID of0—contains the name token “Adams” in association with the user ID 1, thename token “Smith” in association with the user ID 5, and the name token“John” in association with user IDs 1 and 5. Thus, these three entriesin the table 501 indicate that user “Christian Konig” has a directrelationship (e.g., a “friend” relationship) with users “John Adams”(user ID 1) and “John Smith” (user ID 5). Similarly, the second andtenth entries in table 502 together indicate that user “David Eliot” inturn has a direct connection with user “Christian Konig,” and the19^(th), 18^(th), and 4^(th) entries together indicate that “DavidEliot” has a direct connection with “Thomas Stearns Eliot.” Finally, the5^(th) and 6^(th) entries in the table 503 indicate that “Thomas StearnsEliot” likewise has a direct connection with “David Eliot.”

Although the example tables of FIG. 5 only depict information used toidentify connections representing user objects 111, it is appreciatedthat the connection index 121 supports identification of other types ofobjects, as well. For example, the connection index 121 may also containtables containing entries corresponding to pages 115, such as nametokens of the pages and corresponding object identifiers of the pages.In different embodiments, separate object types may have separate setsof tables, or the <name token, ID> entries for different types ofobjects may be stored together within the same table.

Additionally, in one embodiment the connection index 121 includes, foreach table, a version sorted by user ID rather than by name tokens, thuspermitting rapid operations on the user IDs, such as intersections ofuser IDs to find common connections.

Referring again to FIG. 4, in order to identify 410 first-orderconnections of the searching user, the process 400 examines the table ofthe connection index 121 that corresponds to the searching user. Thefirst-order connections are represented by the set of unique user IDsfound within the table. For instance, in the example of FIG. 5 thefirst-order connections of user Christian Konig are the userscorresponding to user IDs 1, 2, 3, 4, 5, 6, 7, and 16.

The process 400 then identifies the second-order connections of thesearching user based on the first-order connections found in step 410.For instance, in the example of FIG. 5, the second-order connections ofuser Christian Konig are the first-order connections of each useridentified as a first-order connection of Christian Konig. Thus, thesecond-order connections of Christian Konig include the first-orderconnections of user David Eliot (user ID 3)—i.e., the users with IDs 8,9, 11, 12, 13, 14, and 15, the user ID 0 being excluded since it refersto the searching user himself.

The process 400 determines 430 the common connections of the searchinguser and each of the second-order connections. Referring again to theexample of FIG. 5, searching user Christian Konig has 3 connections incommon with second-order connection Thomas Stearns Eliot—namely, theusers with IDs 1, 3, and 6. In one embodiment, the query processingmodule 140 stores a list of second-order connections of the searchinguser that have been identified, each entry in the list having a useridentifier and an associated count value, and the list initially emptyat the beginning of the query. The first time that a given second-orderconnection is identified, the corresponding user identifier (or objectidentifier, in the case of a non-user connection, such as a page) isadded to the stored list and the associated count value is set to 1, toreflect that it has been identified once, through one first-orderconnection. Each additional time that the given second-order connectionis identified—i.e., through a different first-order connection—theassociated count value in the list is incremented to reflect anotherfirst-order connection in common between the searching user and thegiven second-order connection. In another one embodiment, the commonconnections between two users are determined by intersecting the userIDs of the users' respective tables.

The process 400 generates 440 the result set based on the identifiedconnections. In one embodiment, the result set comprises the first-orderand second-order connections identified in steps 410-420 and matchingthe query, e.g., having the query as a prefix of one of the name tokensof the connection. The second-order connections are also associated inthe result set with the number of first-order connections that they havein common with the querying user, as determined in step 430. In oneembodiment, the second-order connections are sorted according to thedetermined number of first-order connections that they have in commonwith the searching user and are ordered after the identified first-orderconnections in the result set. In one embodiment, second-orderconnections have fewer than some threshold number of first-orderconnections (e.g., 3) in common with the searching user are removed fromthe result set. The result set is then aggregated with the result setsof other search algorithms, e.g., by the aggregation module 145 of FIG.1.

The foregoing description of the embodiments of the invention has beenpresented for the purpose of illustration; it is not intended to beexhaustive or to limit the invention to the precise forms disclosed.Persons skilled in the relevant art can appreciate that manymodifications and variations are possible in light of the abovedisclosure.

Some portions of this description describe the embodiments of theinvention in terms of algorithms and symbolic representations ofoperations on information. These algorithmic descriptions andrepresentations are commonly used by those skilled in the dataprocessing arts to convey the substance of their work effectively toothers skilled in the art. These operations, while describedfunctionally, computationally, or logically, are understood to beimplemented by computer programs or equivalent electrical circuits,microcode, or the like. Furthermore, it has also proven convenient attimes, to refer to these arrangements of operations as modules, withoutloss of generality. The described operations and their associatedmodules may be embodied in software, firmware, hardware, or anycombinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software modules,alone or in combination with other devices. In one embodiment, asoftware module is implemented with a computer program productcomprising a computer-readable medium containing computer program code,which can be executed by a computer processor for performing any or allof the steps, operations, or processes described.

Embodiments of the invention may also relate to an apparatus forperforming the operations herein. This apparatus may be speciallyconstructed for the required purposes, and/or it may comprise ageneral-purpose computing device selectively activated or reconfiguredby a computer program stored in the computer. Such a computer programmay be stored in a non-transitory, tangible computer readable storagemedium, or any type of media suitable for storing electronicinstructions, which may be coupled to a computer system bus.Furthermore, any computing systems referred to in the specification mayinclude a single processor or may be architectures employing multipleprocessor designs for increased computing capability.

Embodiments of the invention may also relate to a product that isproduced by a computing process described herein. Such a product maycomprise information resulting from a computing process, where theinformation is stored on a non-transitory, tangible computer readablestorage medium and may include any embodiment of a computer programproduct or other data combination described herein.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the invention be limited notby this detailed description, but rather by any claims that issue on anapplication based hereon. Accordingly, the disclosure of the embodimentsof the invention is intended to be illustrative, but not limiting, ofthe scope of the invention, which is set forth in the following claims.

1. A computer-implemented method comprising: accessing a social graphhaving nodes corresponding to objects, and having edges corresponding torelationships of the objects; receiving a query from a client device,the query provided by a user of a social networking system; performing aplurality of search algorithms using the query, wherein one or more ofthe search algorithms obtains results based at least in part onexamining connections of the user in the social networking system;obtaining a result set from each of the search algorithms, wherein eachresult set comprises a set of objects from an object store of the socialnetworking system that match the query; aggregating the result sets fromthe search algorithms into a combined result set; ordering at least aplurality of the objects of the combined result set based at least inpart on measures of affinities of the user for the objects, an affinityof the user for an object comprising at least one from a groupconsisting of: a distance on the social graph between the user and theobject, and a similarity between the user and the object; and providingat least a portion of the combined result set to the client device inresponse to the query.
 2. The computer-implemented method of claim 1,wherein the combined result set comprises a first object having a firsttype and a second object having a second type different from the firsttype.
 3. The computer-implemented method of claim 1, wherein thecombined result set comprises a user object.
 4. The computer-implementedmethod of claim 1, wherein the combined result set comprises a pageobject.
 5. The computer-implemented method of claim 1, wherein thecombined result set comprises at least one of an application object, agroup object, a media object, an event object, and a location object. 6.The computer-implemented method of claim 1, wherein a measure of anaffinity of the user for an object further comprises a physical distancebetween a geographic location associated with the user and a geographiclocation associated with the object.
 7. The computer-implemented methodof claim 1, wherein the objects of the result set are grouped within theresult set based at least in part on a search algorithm that producedthem, the search algorithms including one or more of a second-orderconnections search, a history search, and a global importance search. 8.The computer-implemented method of claim 1, wherein one or more of theobjects in the object store have associated name tokens, and wherein anobject matches the query if the query is a prefix of at least one of theassociated name tokens of the object.
 9. A computer-implemented methodcomprising: receiving a query from a user; submitting the query to aremote social networking system; and receiving, from the socialnetworking system, a combined result set comprising objects matching thequery, the combined result set comprising objects obtained from aplurality of search algorithms performed by the social networkingsystem; wherein at least a plurality of the objects of the combinedresult set are ordered based at least in part on measures of affinitiesof the user for the objects, an affinity of the user for an objectcomprising at least one from a group consisting of: a distance on asocial graph between the user and the object, and a similarity betweenthe user and the object.
 10. The computer-implemented method of claim 9,further comprising using an application programming interface (API) ofthe remote social networking system to log the user in to the socialnetworking system before submitting the query.
 11. Thecomputer-implemented method of claim 9, wherein a measure of an affinityof the user for an object comprises a physical distance between ageographic location associated with the user on the social networkingsystem and a geographic location associated with the object on thesocial networking system.
 12. The computer-implemented method of claim9, wherein the social graph has nodes corresponding to objects and edgescorresponding to relationships of the objects, the user beingrepresented as one of the objects.
 13. The computer-implemented methodof claim 9, wherein the combined result set comprises a first objecthaving a first type and a second object having a second type differentfrom the first type.
 14. A computer-readable storage medium havingexecutable computer program instructions embodied therein, theinstructions for: accessing a social graph having nodes corresponding toobjects, and having edges corresponding to relationships of the objects;receiving a query from a client device, the query provided by a user ofa social networking system; performing a plurality of search algorithmsusing the query, wherein one or more of the search algorithms obtainsresults based at least in part on examining connections of the user inthe social networking system; obtaining a result set from each of thesearch algorithms, wherein each result set comprises a set of objectsfrom an object store of the social networking system that match thequery; aggregating the result sets from the search algorithms into acombined result set; ordering at least a plurality of the objects of thecombined result set based at least in part on measures of affinities ofthe user for the objects, an affinity of the user for an objectcomprising at least one of a group consisting of: a distance on thesocial graph between the user and the object, and a similarity betweenthe user and the object; and providing at least a portion of thecombined result set to the client device in response to the query. 15.The computer-readable storage medium of claim 14, wherein the combinedresult set comprises a first object having a first type and a secondobject having a second type different from the first type.
 16. Thecomputer-readable storage medium of claim 14, wherein a measure of anaffinity of the user for an object further comprises a physical distancebetween a geographic location associated with the user and a geographiclocation associated with the object.
 17. The computer-readable storagemedium of claim 14, wherein the objects of the combined result set aregrouped within the combined result set based at least in part on asearch algorithm that produced them, the search algorithms including oneor more of a second-order connections search, a history search, and aglobal importance search.
 18. The computer-readable storage medium ofclaim 14, wherein one or more of the objects in the object store haveassociated name tokens, and wherein an object matches the query if thequery is a prefix of at least one of the associated name tokens of theobject.
 19. The computer-readable storage medium of claim 14, whereinthe combined result set comprises a first object having a first type anda second object having a second type different from the first type. 20.The computer-readable storage medium of claim 14, wherein the combinedresult set comprises at least one of a user object and a page object.